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Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

Chinese Journal of Chemical Engineering 2009年第1期17卷 95-99页

作者：李修亮苏宏业褚健 National Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang Uni- versity Hangzhou 310027 China

Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

关键词： multiple model soft sensor affinity propagation Gaussian process Bayesian committee machine

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Does calcium diffusional global feedback leads to slow light adaptation in Drosophila photoreceptors? - A 3D biophysical modelling approach

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BMC Neuroscience 2011年第1期12卷 1-1页

作者： Zhuoyi Song Mikko Juusola Daniel Coca SA Billings Marten Postma Roger C Hardie Weiliang Chen Erik De Schutter Department of Biomedical Science University of Sheffield Sheffield S10 2TN UK Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 2TN UK State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing 100875 China Department of Physiology Development and Neuroscience University of Cambridge Cambridge CB2 3DY UK Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0411 Japan Theoretical Neurobiology University of Antwerp 2610 Antwerp Belgium

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Modeling of Isomerization of C_8 Aromatics by Online Least Squares Support Vector Machine

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Chinese Journal of Chemical Engineering 2009年第3期17卷 437-444页

作者：李丽娟苏宏业褚建 College of Automation and Electrical Engineering Nanjing University of Technology State Key Lab. of Industrial Control Technology Institute of Cyber-systems and ControlZhejiang University

The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro-jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of C8 aromatics. The root-mean-square-error (RMSE), number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable.

关键词： least squares support vector machine multi-variable online sparseness isomerization

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Sparse representation based on projection method in online least squares support vector machines

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控制理论与应用（英文版） 2009年第2期7卷 163-168页

作者： Lijuan LI Hongye SU Jian CHU State Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Hangzhou Zhejiang 310027 China College of Automation Nanjing University of Technology Nanjing Jiangsu 210009 China

A sparse approximation algorithm based on projection is presented in this paper in order to overcome the limitation of the non-sparsity of least squares support vector machines （LS-SVM）. The new inputs are projected into the subspace spanned by previous basis vectors （BV） and those inputs whose squared distance from the subspace is higher than a threshold are added in the BV set, while others are rejected. This consequently results in the sparse approximation. In addition, a recursive approach to deleting an exiting vector in the BV set is proposed. Then the online LS-SVM, sparse approximation and BV removal are combined to produce the sparse online LS-SVM algorithm that can control the size of memory irrespective of the processed data size. The suggested algorithm is applied in the online modeling of a pH neutralizing process and the isomerization plant of a refinery, respectively. The detailed comparison of computing time and precision is also given between the suggested algorithm and the nonsparse one. The results show that the proposed algorithm greatly improves the sparsity just with little cost of precision.

关键词： Least squares support vector machines Projection Sparsity pH neutralizing process Isomerization

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A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty

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Chinese Journal of Chemical Engineering 2009年第1期17卷 113-127页

作者：罗春鹏荣冈 State Key Lab. of Industrial Control Technology Institute of Cyber System and Control Zhejiang University Hangzhou 310027 China

A strategy for the integration of production planning and scheduling in refineries is proposed. This strategy relies on rolling horizon strategy and a two-level decomposition strategy. This strategy involves an upper level multiperiod mixed integer linear programming （MILP） model and a lower level simulation system, which is extended from our previous framework for short-term scheduling problems [Luo, C.E, Rong, G, ＂Hierarchical apthis extended framework is to reduce the number of variables and the size of the optimization model and, to quickly find the optimal solution for the integrated planning/scheduling problem in refineries. Uncertainties are also considered in this article. An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.

关键词： refinery planning and scheduling optimization model simulation system

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A Support Vector Regression Approach for Recursive Simultaneous Data Reconciliation and Gross Error Detection in Nonlinear Dynamical systems

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自动化学报 2009年第6期35卷 707-716页

作者： MIAO Yu SU Hong-Ye CHU Jian National Key Laboratory of Industrial Control Teehnology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 P.R. China

The quality of process data in a chemical plant significantly affects the performance and benefits gained from activities like performance monitoring, online optimization, and control. Since many chemical processes often show nonlinear dynamics, techniques like extended Kalman filter (EKF) and nonlinear dynamic data reconciliation (NDDR) have been developed to improve the data quality. Recently, the recursive nonlinear dynamic data reconciliation (RNDDR) technique has been proposed, which combines the merits of EKF and NDDR techniques. However, the RNDDR technique cannot handle measurements with gross errors. In this paper, a support vector (SV) regression approach for recursive simultaneous data reconciliation and gross error detection in nonlinear dynamical systems is proposed. SV regression is a compromise between the empirical risk and the model complexity, and for data reconciliation it is robust to random and gross errors. By minimizing the regularized risk instead of the maximum likelihood in the RNDDR, our approach could achieve not only recursive nonlinear dynamic data reconciliation but also gross error detection simultaneously. The nonlinear dynamic system simulation results in this paper show that the proposed approach is robust, efficient, stable, and accurate for simultaneous data reconciliation and gross error detection in nonlinear dynamic systems within a recursive real-time estimation framework. It can also give better performance of control.

关键词：数据分析自动化系统智能系统质量数据

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Survey on Computational Complexity with Phase Transitions and Extremal Optimization

Survey on Computational Complexity with Phase Transitions an...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Guo-Qiang Zeng Yong-Zai Lu State Key Laboratory of Industrial Control Technology & Institute of Cyber Systems and Control Zhejiang University IEEE

Applying statistical mechanics to search problems in AI, decisions and optimization has been one of the powerful channels to solve NP-hard problems. Extensive analytical and experimental research has shown that the "phase transition" phenomenon in search space is often associated with the hardness of complexity. A Bak-Sneppen(BS) model based general-purpose heuristic method, called extremal optimization(EO), proposed by Boettcher and Percus from physics society may perform very well, especially near the phase transitions in compared with other optimization methods, e.g., genetic algorithm and simulated annealing, etc. To actuate more extensive investigations on this new optimization approach particularly in control, computer and optimization communities,this survey reviews the latest research results from fundamental to practice about the connection between computational complexity and phase transitions. Then, further introduces the concepts, fundamentals, algorithms and applications of EO from its capability of self-organized criticality, backbone analysis and co-evolution moving to a far-from-equilibrium state. Finally, the concluding remarks with suggested future research are illustrated.

关键词： Survey on Computational Complexity with Phase Transitions and Extremal Optimization

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Optimizing Crude Oil Operations under Uncertainty

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IFAC Proceedings Volumes 2009年第4期42卷 1020-1025页

作者： Jishuai Wang Yiping Feng Gang Rong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 P. R. China

This paper presents a novel approach for refinery crude oil operations under uncertainty. Due to the flexibility of the crude oil scheduling, decisions made by deterministic optimizations are often conservative or lack of robustness or even infeasible, so in this paper future uncertainties are considered to improve feasibility and robustness of the schedule. To handle fluctuating product demand and uncertain ship arrival time, deterministic formulation is replaced by chance constrained programming. Through a series of examples, it proves that by using probabilistic programming, the solution of the problem provides a more robust scheduling under a comprehensive confidence level. The relationship between the probability and reliability of a planned operation is also discussed.

关键词： Crude oil operations scheduling optimization model stochastic programming uncertainty

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A New Algorithm for Mining Global Frequent Itemsets in a Stream

A New Algorithm for Mining Global Frequent Itemsets in a Str...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Lichao Guo Hongye Su Yu Qu State Key Laboratory Of Industrial Control Technology Institute of Cyber-Systems and Control University of Zhejiang Hangzhou China

To find global frequent itemsets in a multiple, continuous, rapid and time-varying data stream, a fast, incremental, real-time, and little-memory-cost algorithm should be used. Based on the max-frequency window model, a BHS summary structure and a novel algorithm called GGFI-MFW are proposed. It merely updates the summaries for subsets of the data new arrived and could directly generate the max-frequency for a given item set without scanning the whole summary. Experiment results indicate that the proposed algorithm could efficiently find global frequent itemsets over a data stream with a small memory and perform overwhelming superiority for a large number of distinct items.

关键词： Data mining Itemsets Marketing and sales Fuzzy systems industrial control Ice Frequency measurement Frequency shift keying Research and development

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A Support Vector Regression Approach for Recursive Simultaneous Data Reconciliation and Gross Error Detection in Nonlinear Dynamical systems

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Acta Automatica Sinica 2009年第6期35卷 707-716页

作者： Yu MIAO Hong-Ye SU Jian CHU National Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 P.R. China

关键词： Support vector regression data reconciliation gross error detection nonlinear estimation

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